location-data data freestyle: who in NYC gets up early, who parties late, good spots, and more.

location data is awesome… I have been obsessed with it for a while – I got my first GPS for my bar-mitzvah in 1996 (it was the only thing I asked for other than night vision), in V1 of the internet I got to hang out with early innovators like John Ellenby / GeoVector, and then guys like Mao/Sense networks in 2005 … the first real post on this blog was about location and I have kept on posting graphs off my Garmin watch. When foursquare came out last year the first thing I did was start logging all the checkins I could get my hands on and trying to use the data for stuff like this, and then with Bill Piel and John Steinberg socialgreat started logging millions of them as *i believe* the first released foursquare app. Everyone knows that conceptually location is a huge deal because it is an enormously relevant and relatively un-captured dataset…

the question is, after a decade of trial, is there finally usable sample/insight in the noise? Are we finally getting to the point where ‘location’ is both as ubiquitous and as usable as timestamps?

last weekend my girlfriend and I were trying to figure out what to do on a sunday afternoon, and out of that I was pushed back to looking at my personal foursquare data-set for insights. this is some of the stuff I found using the 27K check-ins logged by a few hundred NYC forusquare ‘friends’ from 2/8/2010 to 5/15/2010 (I use CSVemail.com + email – the most basic API – to continuously log everything in an easy to manipulate format) — I was going to use the 5M checkins logged in socialgreat, but I didn’t feel like opening anything more serious than excel, and heck – 27K data-points from early adopting new yorkers seems like a good start to me…

checkins per day in the set

Checkins per hour in the set of my ‘friends’

Checkins per day per person distribution

Stuff I learned from my cut of 27K checkins at 6.7K locations:1. the top 5% of my friends drive 25% of all checkins (10% -> 38%)
2. the top 1% of locations drive 20% of all checkins (5% -> 43%)

People who get up early (check in highest % of the time between 5 and 9am)1. Andy Weissman
2. Darren Herman
3. Blake Robinson
4. Fred Wilson
5. Jon Steinberg
6. Roger Ehrenberg
7. Jim Moran

Overall if I gut check this, some of it feels right — like the ‘early riser’ list — those are definitely the ‘up and at them folk I would think of in the NYC tech scene that use foursquare… others I am just not hip enough to know about…

but the point (other than that data is fun) — looks like there is some useable data in there… soon enough, location will be every bit as tied to status/sentiment/etc as time is now, and the more dimensions the better in our information.